• Korean Journal of Environment and Ecology
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• v.20 no.3
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• pp.311-318
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• 2006

In an estuary, ecological dynamic is modified for a long time and memorized in soils and landscapes. As landscape memory, ecological motif is defined with dominance and rarity of vegetation. The purpose of the study is to investigate the existing vegetation character and to propose the ecological motif of this area. The present salt-water plants, community species composition and constancy degree around the estuary in Buandam watershed Buan-gun, Jeollabuk-do were analyzed. The results are as follows. The flora of the salt-water plants was listed as 16 taxa; 6 families,13 genera,14 species and 2 varieties. 5 taxa were Gramineae, 4 taxa Chenopodiaceae, and 4 taxa Comrositae. The salt-water plant communities are a total of 10 communities as listed; Zoysia sinica-Artemisia scoparia community, Phacelurus latifolius community, Artemisia scoparia Cnidium japonicum community, Limonium tetragonum -Artemisia scoparia community, Artemisia scoparia community, Suaeda japonica community, Elymus dahuricus community, Suaeda asparagoides community, Zoysia sinica community, and Zoysia sinica-Suaeda japonica community. Analyzed by the community classification species class of constancy degree, Phacelurus latifolius, Cnidium japonicum, Limonium tetragonum, Suaeda japonica, Elymus dahuricus and Suaeda asparagoides belong to I, Zoysia sinica and Artemisia scoparia to II. Elymus dahuricus, Setaria viridis var. pachystachys, Echinochloa crusgalli var. oryzicola, Phacelurus latifolius, Atriplex gmelini, Salicornia herbacea, Calystegia soldanella and Aster tripolium belong to the accompaniment species to I: Zoysia sinica, Suaeda asparagoides, Artemisia capillaris to II; Suaeda japonica, Artemisia scoparia to III ; Cnidium japonicum to IV: Limonium tetragonum, Artemisia fukudo to V. And the ecological motif is the Phacelurus latifolius community.

• IMMUNE NETWORK
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• v.14 no.1
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• pp.21-29
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• 2014

Follicular helper T ($T_{FH}$) cells are recently highlighted as their crucial role for humoral immunity to infection as well as their abnormal control to induce autoimmune disease. During an infection, na$\ddot{i}$ve T cells are differentiating into $T_{FH}$ cells which mediate memory B cells and long-lived plasma cells in germinal center (GC). $T_{FH}$ cells are characterized by their expression of master regulator, Bcl-6, and chemokine receptor, CXCR5, which are essential for the migration of T cells into the B cell follicle. Within the follicle, crosstalk occurs between B cells and $T_{FH}$ cells, leading to class switch recombination and affinity maturation. Various signaling molecules, including cytokines, surface molecules, and transcription factors are involved in $T_{FH}$ cell differentiation. IL-6 and IL-21 cytokine-mediated STAT signaling pathways, including STAT1 and STAT3, are crucial for inducing Bcl-6 expression and $T_{FH}$ cell differentiation. $T_{FH}$ cells express important surface molecules such as ICOS, PD-1, IL-21, BTLA, SAP and CD40L for mediating the interaction between T and B cells. Recently, two types of microRNA (miRNA) were found to be involved in the regulation of $T_{FH}$ cells. The miR-17-92 cluster induces Bcl-6 and $T_{FH}$ cell differentiation, whereas miR-10a negatively regulates Bcl-6 expression in T cells. In addition, follicular regulatory T ($T_{FR}$) cells are studied as thymus-derived $CXCR5^+PD-1^+Foxp3^+\;T_{reg}$ cells that play a significant role in limiting the GC response. Regulation of $T_{FH}$ cell differentiation and the GC reaction via miRNA and $T_{FR}$ cells could be important regulatory mechanisms for maintaining immune tolerance and preventing autoimmune diseases such as systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). Here, we review recent studies on the various factors that affect $T_{FH}$ cell differentiation, and the role of $T_{FH}$ cells in autoimmune diseases.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.133-144
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• 2020

The number of elderly people with dementia is increasing as fast as the proportion of older people due to aging, which creates a social and economic burden. In particular, dementia care costs, including indirect costs such as increased care costs due to lost caregiver hours and caregivers, have grown exponentially over the years. In order to reduce these costs, it is urgent to introduce a management system to care for dementia patients. Therefore, this study proposes a sensor-based abnormal behavior detection system to manage dementia patients who live alone or in an environment where they cannot always take care of dementia patients. Existing studies were merely evaluating behavior or evaluating normal behavior, and there were studies that perceived behavior by processing images, not data from sensors. In this study, we recognized the limitation of real data collection and used both the auto-encoder, the unsupervised learning model, and the LSTM, the supervised learning model. Autoencoder, an unsupervised learning model, trained normal behavioral data to learn patterns for normal behavior, and LSTM further refined classification by learning behaviors that could be perceived by sensors. The test results show that each model has about 96% and 98% accuracy and is designed to pass the LSTM model when the autoencoder outlier has more than 3%. The system is expected to effectively manage the elderly and dementia patients who live alone and reduce the cost of caring.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.99-120
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• 2017

In order to develop a semiautomatic support system that allows researchers concerned to efficiently analyze the technical trends for the ever-growing industry and market. This paper introduces a couple of Korean sentence generation models that can automatically generate definitional statements as well as descriptions of technical terms and concepts. The proposed models are based on a deep learning model called LSTM (Long Sort-Term Memory) capable of effectively labeling textual sequences by taking into account the contextual relations of each item in the sequences. Our models take technical terms as inputs and can generate a broad range of heterogeneous textual descriptions that explain the concept of the terms. In the experiments using large-scale training collections, we confirmed that more accurate and reasonable sentences can be generated by CHAR-CNN-LSTM model that is a word-based LSTM exploiting character embeddings based on convolutional neural networks (CNN). The results of this study can be a force for developing an extension model that can generate a set of sentences covering the same subjects, and furthermore, we can implement an artificial intelligence model that automatically creates technical literature.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.4
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• pp.56-67
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• 2004

This study analyzes human error of workers performing simple repetitive tasks, and in order to prepare preventative measures, 486 people were used as subjects. The results of the study are like the following. First, the biggest cause of human error showed to be the worker himself in $77.8\%$ of the cases, machinery showed to be the cause in $16.3\%$ of the cases and management showed to be the cause in $6.0\%$ of the cases. The results show that most of the human error occurred due to the worker performing simple repetitive tasks and the human errors showed to be caused more by bad ergonomics and long hours rather than by problems with machinery. In addition, the area with the highest rate of human error showed to be the Human Information Processing System with Task Input Error being the highest with $46.9\%$, followed by Judgement and Memory Error with $36.4\%$ and Recognition Verification Error with $16.7\%$. Although fully automated tasks may reduce the rate of human error we must focus on lowering the rate of problems arising from spontaneous errors caused by workers performing simple repetitive tasks by continuously renewing plans and budgets in order to standardize tasks by incorporating cyclic positioning according to experience and positional exchange and by inspecting the workplace to increase efficiency of the workers.

• Biomolecules & Therapeutics
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• v.15 no.1
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• pp.16-26
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• 2007

Tissue plasminogen activator (tPA) is a serine protease catalyzing the proteolytic conversion of plasminogen into plasmin, which is involved in thrombolysis. During last two decades, the role of tPA in brain physiology and pathology has been extensively investigated. tPA is expressed in brain regions such as cortex, hippocampus, amygdala and cerebellum, and major neural cell types such as neuron, astrocyte, microglia and endothelial cells express tPA in basal status. After strong neural stimulation such as seizure, tPA behaves as an immediate early gene increasing the expression level within an hour. Neural activity and/or postsynaptic stimulation increased the release of tPA from axonal terminal and presumably from dendritic compartment. Neuronal tPA regulates plastic changes in neuronal function and structure mediating key neurologic processes such as visual cortex plasticity, seizure spreading, cerebellar motor learning, long term potentiation and addictive or withdrawal behavior after morphine discontinuance. In addition to these physiological roles, tPA mediates excitotoxicity leading to the neurodegeneration in several pathological conditions including ischemic stroke. Increasing amount of evidence also suggest the role of tPA in neurodegenerative diseases such as Alzheimer's disease and multiple sclerosis even though beneficial effects was also reported in case of Alzheimer's disease based on the observation of tPA-induced degradation of $A{\beta}$ aggregates. Target proteins of tPA action include extracellular matrix protein laminin, proteoglycans and NMDA receptor. In addition, several receptors (or binding partners) for tPA has been reported such as low-density lipoprotein receptor-related protein (LRP) and annexin II, even though intracellular signaling mechanism underlying tPA action is not clear yet. Interestingly, the action of tPA comprises both proteolytic and non-proteolytic mechanism. In case of microglial activation, tPA showed non-proteolytic cytokine-like function. The search for exact target proteins and receptor molecules for tPA along with the identification of the mechanism regulating tPA expression and release in the nervous system will enable us to better understand several key neurological processes like teaming and memory as well as to obtain therapeutic tools against neurodegenerative diseases.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.183-187
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• 2017

Smart phone users prefer fast reading and texting. Hence, users frequently use abbreviated sequences of words and phrases. Nowadays, abbreviations are widely used from chat terms to technical terms. Therefore, gathering abbreviations would be helpful to many services, including information retrieval, recommendation system, and so on. However, manually gathering abbreviations needs to much effort and cost. This is because new abbreviations are continuously generated whenever a new material such as a TV program or a phenomenon is made. Thus it is required to generate of abbreviations automatically. To generate Korean abbreviations, the existing methods use the rule-based approach. The rule-based approach has limitations, in that it is unable to generate irregular abbreviations. Another problem is to decide the correct abbreviation among candidate abbreviations generated rules. To address the limitations, we propose a method of generating Korean abbreviations automatically using sequence-to-sequence learning in this paper. The sequence-to-sequence learning can generate irregular abbreviation and does not lead to the problem of deciding correct abbreviation among candidate abbreviations. Accordingly, it is suitable for generating Korean abbreviations. To evaluate the proposed method, we use dataset of two type. As experimental results, we prove that our method is effective for irregular abbreviations.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.109-120
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• 1998

An image segmentation approach based on the fuzzy worm searching and MIN-MAX clustering algorithm is proposed in this paper. This algorithm deals with fuzzy worm value and min-max node at a gross scene level, which investigates the edge information including fuzzy worm action and spatial relationship of the pixels as the parameters of its objective function. But the conventional segmentation methods for edge extraction generally need the mask information for the algebraic model, and take long run times at mask operation, whereas the proposed algorithm has single operation according to active searching of fuzzy worms. In addition, we also propose both genetic fuzzy worm searching and genetic min-max clustering using genetic algorithm to complete clustering and fuzzy searching on grey-histogram of image for the optimum solution, which can automatically determine the size of ranges and has both strong robust and speedy calculation. The simulation results showed that the proposed algorithm adaptively divided the quantized images in histogram region and performed single searching methods, significantly alleviating the increase of the computational load and the memory requirements.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.27-40
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• 2020

In this paper, we investigate the correlation between the amount of English sentences that Korean English learners (L2ers) are exposed to and their sentence processing patterns by examining what Long Short-Term Memory (LSTM) language models (LMs) can learn about implicit syntactic relationship: that is, the filler-gap dependency. The filler-gap dependency refers to a relationship between a (wh-)filler, which is a wh-phrase like 'what' or 'who' overtly in clause-peripheral position, and its gap in clause-internal position, which is an invisible, empty syntactic position to be filled by the (wh-)filler for proper interpretation. Here to implement L2ers' English learning, we build LSTM LMs that in turn learn a subset of the known restrictions on the filler-gap dependency from English sentences in the L2 corpus that L2ers can potentially encounter in their English learning. Examining LSTM LMs' behaviors on controlled sentences designed with the filler-gap dependency, we show the characteristics of L2ers' sentence processing using the information-theoretic metric of surprisal that quantifies violations of the filler-gap dependency or wh-licensing interaction effects. Furthermore, comparing L2ers' LMs with native speakers' LM in light of processing the filler-gap dependency, we not only note that in their sentence processing both L2ers' LM and native speakers' LM can track abstract syntactic structures involved in the filler-gap dependency, but also show using linear mixed-effects regression models that there exist significant differences between them in processing such a dependency.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.36-50
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• 2005

e-Learning has been mushrooming with wide range of learning groups from pedagogy to andragogy. Despite of increasing e-learning opportunities, many people doubt whether e-learning learners really learn something. The related research papers emphasized that e-Learning would be a failure in terms of understanding of e-learners and intuitive learning activities for activating learner's long-term memory span. The current learning strategies in e-Learning may be based on the traditional classroom, and this results in boring and ineffective learning outcomes. This paper analyzed that how learners have received e-Learning for the last few years from the research and explained what could be the failing aspects of e-Learning. To be successful, e-Learning should consider the e-Learner's individualized teaming style and thinking patterns. When considering of various e-Learning components, the quality of e-learning should not be focused on any specific single factor, but develop every individual factor to the high level of quality. In conclusion, this paper suggest that we need new understand of e-Learning and e-Learner. Also the e-Learning strategies should be examined throughly whether they are on the side of learners and realized how they learn from e-Learning. Finally, we should add enormous imagination into e-Learning for next generation because their teaming patterns significantly differ from their parent's generation.